CEC 2007 Poster
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Interactive genetic algorithms are used to evolve user interface designs. We explore ways to mitigate user fatigue by asking for user input every n generations.
![What Kind of UIs do Users Evolve? Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Acknowledgments: This material is based in part upon work supported by the Office of Naval Research under contract number N00014-03-1-0104 and in part upon work supported by the National Science Foundation under Grant No. 0447416. Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Acknowledgments: This material is based in part upon work supported by the Office of Naval Research under contract number N00014-03-1-0104 and in part upon work supported by the National Science Foundation under Grant No. 0447416. Interactive Evolutionary Approach for UI Design We use an interactive genetic algorithm (IGA) to help user interface designers explore the space of UIs. User interface designers are guided by both objective metrics, obtained from guidelines of style, and subjective metrics, obtained from the designer’s expertise, intuition, and emotions. Our interactive genetic algorithm combines both objective and subjective heuristics to evolve user interfaces in order to expose the designer to various designs and to provide creativity and insight. ,[object Object],[object Object],[object Object],[object Object],[object Object],Generation 0 Generation 30 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],What is t? We explore the effects of varying the frequency of user input every t th generation. With higher values of t we can accelerate the population convergence and reduce user fatigue by asking for less user input. How Does t Affect the Fitness Performance? For all three users, using a value of t greater than 1 results in the best fitness performance. Thus, asking for less user input reduces user fatigue without compromising fitness performance. The average fitness shows drops in fitness associated with the time steps on which the users provide feedback. CEC 2007 How Do We Encode UIs in the IGA? Interactive Genetic Algorithms How Does the User Provide Feedback?](https://image.slidesharecdn.com/cec-2007-poster2494/85/cec-2007-poster-1-320.jpg)
Interactive genetic algorithms are used to evolve user interface designs. We explore ways to mitigate user fatigue by asking for user input every n generations.
